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The term “productivity” all too often has becomes a buzz-word, ultimately diminishing its perceived importance. However, productivity is the major concern of any team, and therefore must be defined to gain an appropriate understanding of how a system is actually working. Here, productivity means the level of contribution to the throughput of a system such as defined in the Theory of Constraints. In the field of space exploration, the throughput is the number of milestones of the mission accomplished as well as the potential survival during extreme events (due to failures or other unplanned events). For a time tasks were accomplished by expert individuals (e.g., an astronaut), but recently team structures have become the norm. It is clear that with increased mission complexity, “no single entity can have complete knowledge of or the abilities to handle all matters” [10].

The application of Statistical Process Control and Design of Experiment methods in the research laboratory can lead to significant gains in the Powertrain development process. Empirical methods such as Design of Experiments, Regression, and Neural Network techniques can be applied to help researchers gain better understanding of the cause and effect relationships of emission, alternative fuel source, performance, fuel economy, and engine management system - calibration studies. The use of these empirical modeling techniques along with model based Genetic Algorithm, Gradient, or Constraint based solution search methods will help identify the “process settings” that improve fuel economy, improve performance, and reduce pollutants. Since empirical methods are fundamentally based on the acquired test data, it is vitally important that the laboratory measurements are repeatable, consistent, and void of sources of variance that have a significant effect on the acquired test data.

The future of human exploration in the solar system is contingent on the ability to exploit resources in-situ to produce mission consumables. Specifically, it has become clear that the success of a manned mission to Mars will likely depend on fuel components created on the Martian surface. While several architectures for an unmanned fuel production surface facility on Mars exist in theory, a simulation of the performance and operation of these architectures has not been created. In this paper, the framework describing a simulation of one such architecture is defined. Within this architecture, each component of the base is implemented as a state machine, with the ability to communicate with other base elements as well as a supervisor. An environment supervisor is also created which governs low level aspects of the simulation such as movement and resource distribution, in addition to higher-level aspects such as location selection with respect to operations specific behavior.

The National Aeronautics and Space Administration (NASA) is preparing for a manned mission to Mars to test the sustainment of civilization on the planet Mars. This research explores the requirements and feasibility of autonomously producing fuel on Mars for a return trip back to Earth. As a part of NASA’s initiative for a manned trip to Mars, our team’s work creates and analyzes the allocation of resources necessary in deploying a fuel station on this foreign soil. Previous research has addressed concerns with a number individual components of this mission such as power required for fuel station and tools; however, the interactions between these components and the effects they would have on the overall requirements for the fuel station are still unknown to NASA. By creating a baseline discrete-event simulation model in a simulation software environment, the research team has been able to simulate the fuel production process on Mars.

In this study we consider the coordinated exploration of an unfamiliar Martian landscape by a swarm of small autonomous rovers, called Swarmies, simulated in a distributed setting. With a sustainable program of return missions to and from Mars in mind, the goal of said exploration is to efficiently prospect the terrain for water meant to be gathered and then utilized in the production of rocket fuel. The rovers are tasked with relaying relevant data to a home base that is responsible for maintaining a mining schedule for an arbitrarily large group of rovers extracting water-rich regolith. For this reason, it is crucial that the participants maintain a wireless connection with one another and with the base throughout the entire process. We describe the architecture of our simulation which is composed of HLA-compliant components that are visualized via the Distributed Observer Network tool developed by NASA.